wsrf: Weighted Subspace Random Forest for Classification

A parallel implementation of Weighted Subspace Random Forest. The
Weighted Subspace Random Forest algorithm was proposed in the
International Journal of Data Warehousing and Mining by Baoxun Xu,
Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye
(2012) <doi:10.4018/jdwm.2012040103>. The algorithm can classify
very high-dimensional data with random forests built using small
subspaces. A novel variable weighting method is used for variable
subspace selection in place of the traditional random variable
sampling.This new approach is particularly useful in building
models from high-dimensional data.